Flexible Spending Accounts (FSAs) allow employees to allocate pre-tax dollars into a special account to be used on medical expenses during a calendar year. The potential financial benefit to consumers from the use of FSAs can be quite large since consumers avoid all income, social security and medicare taxes on FSA monies. However, the amount put into a FSA must be specified in advance of the calendar year and any unused funds are lost at the end of the year, thus creating some unusual incentives for consumers. This project seeks to model an individual's optimal contribution into a FSA as a function of tax rates, income, insurance plan characteristics, demographic characteristics and other variables which might affect risk. A simple version allows for direct analysis, while a richer model is analyzed using simulation methods. A large, private data set allows for testing the predictions of the model and for a detailed characterization of actual behavior. The data used is unusual in that it specifies both the amount of the contribution and the date and reason (specific illness) for the withdrawal, as well as important demographic information. This project will evaluate the overall effects of FSAs on medical spending and the choice of insurance plan as well as model the contribution decision itself.